
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import *
from matplotlib.pyplot import figure
import numpy as np
import matplotlib.colors as mcolors
import calendar
from datetime import datetime


#Used to view long lists of output if needed
#pd.set_option('display.height', 1000)
#pd.set_option('display.max_rows', 500)
#pd.set_option('display.max_columns', 500)
#pd.set_option('display.width', 1000)


Complete_HWC_Data = pd.read_csv("/Users/nerdbear/Downloads/Complete_HWC_Data.csv", index_col=0, dtype=str, encoding='utf8')
Complete_HWC_Data[Complete_HWC_Data.columns[0:20]] = Complete_HWC_Data[Complete_HWC_Data.columns[0:20]].astype("str")

Complete_HWC_Data["Sum of Number of Animals"] = Complete_HWC_Data["Sum of Number of Animals"].astype("float")
Complete_HWC_Data["Total Staff Hours"] = Complete_HWC_Data["Total Staff Hours"].astype("float")
Complete_HWC_Data["Total Staff Involved"] = Complete_HWC_Data["Total Staff Involved"].astype("float")
Complete_HWC_Data["Latitude Public"] = Complete_HWC_Data["Latitude Public"].astype("float")
Complete_HWC_Data["Longitude Public"] = Complete_HWC_Data["Longitude Public"].astype("float")
#Complete_HWC_Data["Species Common Name"] = Complete_HWC_Data["Species Common Name"].astype("str")
Complete_HWC_Data[Complete_HWC_Data.columns[20:158]] = Complete_HWC_Data[Complete_HWC_Data.columns[20:170]].astype("float")
#Complete_HWC_Data["Animal Health Status"] = Complete_HWC_Data["Animal Health Status"].astype("str")
#Complete_HWC_Data["Cause of Animal Health Status"] = Complete_HWC_Data["Cause of Animal Health Status"].astype("str")
Complete_HWC_Data.head()


Complete_HWC_Data_Month = Complete_HWC_Data.loc[:, ("Incident Type", "Field Unit", "Incident Date")]
Complete_HWC_Data_Month["Incident Month"] = pd.to_datetime(Complete_HWC_Data_Month["Incident Date"]).dt.to_period("M")
Complete_HWC_Data_Month["Incident Month"] = Complete_HWC_Data_Month["Incident Month"].astype("str")
Complete_HWC_Data_Month["Incident Month"] = pd.to_datetime(Complete_HWC_Data_Month["Incident Month"])
#Complete_HWC_Data_Month
Complete_HWC_Data_Month = Complete_HWC_Data_Month.set_index("Incident Date")
Complete_HWC_Data_Month["Incident Month"] = pd.DatetimeIndex(Complete_HWC_Data_Month.index).month
#Complete_HWC_Data_Month
Complete_HWC_Data_Month = Complete_HWC_Data_Month.replace({'Incident Month' : { 1 : "January", 2 : "February", 3 : "March", 4 : "April", 5 : "May", 6 : "June", 7 : "July", 8 : "August", 9 : "September", 10 : "October", 11 : "November", 12 : "December"}})
Complete_HWC_Data_Month["Incident Month"] = Complete_HWC_Data_Month["Incident Month"].astype('category')
Complete_HWC_Data_Month["Incident Month"] = Complete_HWC_Data_Month["Incident Month"].cat.reorder_categories(["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"], ordered=True)
Complete_HWC_Data_Month["Incident Month"].cat.categories

IncidentsByMonth = Complete_HWC_Data_Month["Incident Type"].groupby([Complete_HWC_Data_Month["Incident Month"], Complete_HWC_Data_Month["Incident Type"]]).count().reset_index(name="count")
IncidentsByMonth = IncidentsByMonth.pivot_table("count", "Incident Month", "Incident Type")
#IncidentsByMonth

Complete_HWC_Data_Year = Complete_HWC_Data.loc[:, ("Incident Type", "Field Unit", "Incident Date", "Protected Heritage Area")]
Complete_HWC_Data_Year["Incident Year"] = pd.to_datetime(Complete_HWC_Data_Year["Incident Date"]).dt.to_period("Y")
Complete_HWC_Data_Year["Incident Year"] = Complete_HWC_Data_Year["Incident Year"].astype("str")
Complete_HWC_Data_Year["Incident Year"] = pd.to_datetime(Complete_HWC_Data_Year["Incident Year"])
#Complete_HWC_Data_Year

IncidentsByYear = Complete_HWC_Data_Year["Incident Type"].groupby([Complete_HWC_Data_Year["Incident Year"], Complete_HWC_Data_Year["Incident Type"]]).count().reset_index(name="count")
#IncidentsByYear
IncidentsByYear = IncidentsByYear.pivot_table("count", "Incident Year", "Incident Type")
#IncidentsByYear

HighIncParks = IncidentsByPark.loc[IncidentsByPark["count"] >1000]
#HighIncParks

Parks = Complete_HWC_Data_Year["Protected Heritage Area"].unique()
#Parks

IncidentsByPark = Complete_HWC_Data_Year["Protected Heritage Area"].groupby([Complete_HWC_Data_Year["Protected Heritage Area"]]).count().reset_index(name="count")
IncidentsByPark = IncidentsByPark.pivot_table("count", "Protected Heritage Area")
#IncidentsByPark.index
IncidentsByPark=IncidentsByPark.sort_values(by=['count'])
#IncidentsByPark

IncidentsByYearByPark = Complete_HWC_Data_Year["Protected Heritage Area"].groupby([Complete_HWC_Data_Year["Incident Year"], Complete_HWC_Data_Year["Protected Heritage Area"]]).count().reset_index(name="count")
IncidentsByYearByPark = IncidentsByYearByPark.pivot_table("count", "Incident Year", "Protected Heritage Area")
#IncidentsByYearByPark

IncidentsByTypeByPark = Complete_HWC_Data_Year["Incident Type"].groupby([Complete_HWC_Data_Year["Protected Heritage Area"], Complete_HWC_Data_Year["Incident Type"]]).count().reset_index(name="count")
IncidentsByTypeByPark = IncidentsByTypeByPark.sort_values(by="count")
IncidentsByTypeByPark = IncidentsByTypeByPark.sort_values(by="Protected Heritage Area")
IncidentsByPark=IncidentsByPark.sort_values(by=['count',])
#IncidentsByTypeByPark

IncidentsByTypeByPark = IncidentsByTypeByPark.pivot_table("count", "Protected Heritage Area","Incident Type",)
#IncidentsByTypeByPark

##
#The for loop I use below doesn't function how I wanted - and I ran out of time to get it 
#to do exactly what I wanted it to do (count frequency of Incident Types by Species). 
#I'm still using the output of the loop as it does provide counts by species, and 
#can be used to plot only the species that are included in more than 100 incidents. 
#If I can get the for loop working properly later on, I'll be able to plot the Species 
#and Incident Types better. ValueCounts = Complete_HWC_Data["Species Common Name"].value_counts()

SpeciesData = Complete_HWC_Data.loc[:, ("Incident Type", "Species Common Name")]

Counts = []
for i in Complete_HWC_Data["Species Common Name"]:
                Counts.append(ValueCounts[i])
SpeciesData.insert(0, "Species_Counts", Counts)
#SpeciesData

HighSpeciesData = SpeciesData.loc[SpeciesData["Species_Counts"] > 100]
HighSpeciesData=HighSpeciesData.sort_values(by=['Species_Counts'])
HighSpeciesCount = HighSpeciesData["Species Common Name"].unique()
#HighSpeciesCount

###

IncBySpecies = Complete_HWC_Data.loc[:, ("Incident Type", "Species Common Name")]
IncBySpecies = IncBySpecies["Incident Type"].groupby([IncBySpecies["Species Common Name"], IncBySpecies["Incident Type"]]).count().reset_index(name="count")
IncBySpecies = IncBySpecies.set_index("Incident Type")
#Complete_HWC_Data["Incident Type"].loc[Complete_HWC_Data["Species Common Name"]=="Black Bear"]
IncBySpecies = IncBySpecies.pivot_table("count", "Incident Type", "Species Common Name")


#IncBySpecies

HealthBySpecies = Complete_HWC_Data.loc[:, ("Animal Health Status", "Species Common Name")]
HealthBySpecies = HealthBySpecies["Animal Health Status"].groupby([HealthBySpecies["Species Common Name"], HealthBySpecies["Animal Health Status"]]).count().reset_index(name="count")
HealthBySpecies = HealthBySpecies.loc[HealthBySpecies["Animal Health Status"] != "Not Applicable"]
HealthBySpecies = HealthBySpecies.set_index("Animal Health Status")
#Complete_HWC_Data["Incident Type"].loc[Complete_HWC_Data["Species Common Name"]=="Black Bear"]
HealthBySpecies = HealthBySpecies.pivot_table("count", "Animal Health Status", "Species Common Name")
#HealthBySpecies 

SpeciesByHealth = Complete_HWC_Data.loc[:, ("Animal Health Status", "Species Common Name")]
SpeciesByHealth = SpeciesByHealth["Animal Health Status"].groupby([SpeciesByHealth["Species Common Name"], SpeciesByHealth["Animal Health Status"]]).count().reset_index(name="count")
SpeciesByHealth = SpeciesByHealth.loc[SpeciesByHealth["Animal Health Status"] != "Not Applicable"]
SpeciesByHealth = SpeciesByHealth.set_index("Species Common Name")
SpeciesByHealth = SpeciesByHealth.pivot_table("count","Species Common Name", "Animal Health Status")
#SpeciesByHealth


plt.figure(figsize=(10,5));
plt.hist(Complete_HWC_Data["Field Unit"], width = 0.8, bins = 19, color = "green")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18)
plt.xlabel('Field Unit', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Field Unit', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Protected Heritage Area"], width = 0.8, bins = 35, color = "lightseagreen")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18)
plt.xlabel('Canadian National Park', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Canadian National Park', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Incident Type"], width = 0.8, bins = 9, color = "cornflowerblue")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18)
plt.xlabel('Incident Type', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Incident Type', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Latitude Public"],width=2, bins=15, color = "goldenrod")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18)
plt.xlabel('Latitude', fontweight="bold", size = 18);
plt.title('Total Number of Incidents by Latitude', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Longitude Public"], width=2, bins=15, color = "tomato")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18)
plt.xlabel('Longitude', fontweight="bold", size = 18);
plt.title('Total Number of Incidents by Longitude', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Animal Health Status"], width = 0.8, bins = 9, color="olivedrab")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Animal Health Status', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Animal Health Status', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,10));
plt.hist(Complete_HWC_Data["Cause of Animal Health Status"], width = 0.8, bins = 18, color="lightsteelblue")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Cause of Animal Health Status', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Cause of Animal Health Status', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()

plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Cause of Animal Health Status"].loc[Complete_HWC_Data["Cause of Animal Health Status"] != "nan"], width = 0.8, bins = 18, color="lightsteelblue")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Cause of Animal Health Status', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Cause of Animal Health Status (Missing Values Removed)', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Animal Behaviour"], width = 0.8, bins = 22, color="tan")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Animal Behaviour', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Animal Behaviour', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()

plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Animal Behaviour"].loc[Complete_HWC_Data["Animal Behaviour"] != "nan"], width = 0.8, bins = 22, color="tan")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Animal Behaviour', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Animal Behaviour (Missing Values Removed)', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Reason for Animal Behaviour"], width = 0.8, bins = 17, color="palegreen")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Reason for Animal Behaviour', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Reason for Animal Behaviour', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()

plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Reason for Animal Behaviour"].loc[Complete_HWC_Data["Reason for Animal Behaviour"] != "nan"], width = 0.8, bins = 16, color="palegreen")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Reason for Animal Behaviour', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Reason for Animal Behaviour (Missing Values Removed)', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Animal Attractant"], width = 0.8, bins = 20, color="palevioletred")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Animal Attractant', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Animal Attractant', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()

plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Animal Attractant"].loc[Complete_HWC_Data["Animal Attractant"] != "nan"], width = 0.8, bins = 19, color="palevioletred")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Animal Attractant', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Animal Attractant (Missing Values Removed)', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Deterrents Used"], width = 0.8, bins = 26, color="plum")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Deterrents Used', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Deterrents Used', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()

plt.figure(figsize=(10,7));
plt.hist(Complete_HWC_Data["Deterrents Used"].loc[Complete_HWC_Data["Deterrents Used"] != "nan"], width = 0.8, bins = 25, color="plum")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Deterrents Used', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Deterrents Used (Missing Values Removed)', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(12,7));
plt.hist(Complete_HWC_Data["Animal Response to Deterrents"], width = 0.8, bins = 11, color="lightskyblue")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18);
plt.xlabel('Animal Response to Deterrents', fontweight="bold", size = 18);
plt.title('Total Number of Incidents per Animal Response to Deterrents', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


plt.figure(figsize=(20,10));
plt.hist(HighSpeciesData["Species Common Name"], width = 0.9, bins = 36, color="lightcoral")
plt.ylabel('Number of Incidents', fontweight="bold", size = 18)
plt.xlabel('Species', fontweight="bold", size = 18);
plt.title('Total Number of Incidents by Species Common Name', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.show()


#After the basic histogram above and seeing how few incidents several parks had, 
#I wanted to view the exact number of incidents that occured in each park. 

x = IncidentsByPark.index
y = IncidentsByPark["count"]

fig,ax = plt.subplots(figsize=(35,20))
plt.plot(x, y, label=IncidentsByPark.columns, marker="o", mew=3, linewidth=8, color = "lightseagreen")
plt.xlabel("National Park", fontweight="bold", size = 26)
plt.ylabel("Number of Incidents",  fontweight="bold", size = 26)
plt.xticks(size=20, rotation="vertical", label=IncidentsByPark["count"])
plt.yticks(size=20)
plt.title("Total Number of Incidents Per National Park", fontweight="bold", size = 30)
for index in range(len(x)):
  ax.text(x[index], y[index], y[index], size=26)
plt.show()


plt.figure(figsize=(12,7));
plt.plot(HealthBySpecies["Black Bear"], label="Black Bear", marker="o", mew=8, linewidth=2, color = "violet")
plt.plot(HealthBySpecies["Elk"], label="Elk", marker="o", mew=8, linewidth=2)
plt.plot(HealthBySpecies["Grizzly Bear"], label="Grizzly Bear", marker="o", mew=8, linewidth=2)
plt.plot(HealthBySpecies["Mule Deer"], label="Mule Deer", marker="o", mew=8, linewidth=2)
plt.ylabel('Number of Incidents', fontweight="bold", size = 18)
plt.xlabel('Incident Type', fontweight="bold", size = 18);
plt.title('Total Number of Incidents by Health Status for 4 Most Frequent Species', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.legend(prop={"size":16});
plt.show()


plt.figure(figsize=(12,7));
plt.plot(IncBySpecies["Black Bear"], label="Black Bear", marker="o", mew=8, linewidth=2, color="violet")
plt.plot(IncBySpecies["Elk"], label="Elk", marker="o", mew=8, linewidth=2)
plt.plot(IncBySpecies["Grizzly Bear"], label="Grizzly Bear", marker="o", mew=8, linewidth=2)
plt.plot(IncBySpecies["Mule Deer"], label="Mule Deer", marker="o", mew=8, linewidth=2)
plt.ylabel('Number of Incidents', fontweight="bold", size = 18)
plt.xlabel('Incident Type', fontweight="bold", size = 18);
plt.title('Total Number of Incidents by Type for 4 Most Frequent Species', fontweight="bold", size = 22)
plt.xticks(size=16, rotation='vertical')
plt.yticks(size=16)
plt.legend(loc="upper right", prop={"size":16});
plt.show()


#Modifying color cycler so that the colors are not repeating across different parks.
plot_colors = ["mediumaquamarine", "darkcyan", "deepskyblue", "firebrick", "mediumpurple", "darkorchid", "magenta", "red", "sienna", "saddlebrown", "peru", "darkorange", "tan", "goldenrod", "gold", "darkkhaki", "olive", "yellowgreen", "olivedrab", "chartreuse", "darkseagreen", "palegreen", "forestgreen", "limegreen", "mediumspringgreen"]
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plot_colors)


plt.figure(figsize=(25,15));
plt.plot(IncidentsByTypeByPark, label=IncidentsByTypeByPark.columns, marker="o", mew=8, linewidth=4);
plt.xlabel("National Park", fontweight="bold", size = 22);
plt.ylabel("Number of Incidents", fontweight="bold", size = 22);
plt.xticks(size=16, rotation="vertical")
plt.yticks(size=16)
plt.legend(loc="upper right", prop={"size":22});
plt.title("Number of Incidents by National Park, by Incident Type", fontweight="bold", size = 30);
plt.figure().set_figheight(20);
plt.show();

<Figure size 640x2000 with 0 Axes>


plot_colors = ["mediumaquamarine", "darkcyan", "deepskyblue", "firebrick", "mediumpurple", "darkorchid", "violet", "magenta", "deeppink", "palevioletred", "rosybrown", "lightcoral", "red", "sienna", "saddlebrown", "peru", "darkorange", "tan", "goldenrod", "gold", "darkkhaki", "olive", "yellowgreen", "olivedrab", "chartreuse", "darkseagreen", "palegreen", "forestgreen", "limegreen", "mediumspringgreen"]
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plot_colors)

#Plot elongated to be able to better view the distinct lines with lower values.
plt.figure(figsize=(15,25));
plt.plot(IncidentsByYear, label=IncidentsByYear.columns, marker="o", mew=8, linewidth=3);
plt.xlabel("Incident Date", fontweight="bold", size = 22);
plt.ylabel("Number of Incidents", fontweight="bold", size = 22);
plt.xticks(size=16)
plt.yticks(size=16)
plt.legend(loc="upper left", prop={"size":22});
plt.title("Number of Incidents Per Year, by Incident Type", fontweight="bold", size = 30);
plt.figure().set_figheight(20);
plt.show();

<Figure size 640x2000 with 0 Axes>


#Modifying color cycler so that the colors are not repeating across different parks.
plot_colors = ["mediumaquamarine", "mediumturquoise", "darkcyan", "deepskyblue", "steelblue", "mediumpurple", "darkorchid", "violet", "magenta", "deeppink", "palevioletred", "rosybrown", "lightcoral", "firebrick", "red", "sienna", "saddlebrown", "peru", "darkorange", "tan", "goldenrod", "gold", "darkkhaki", "olive", "yellowgreen", "olivedrab", "chartreuse", "darkseagreen", "palegreen", "forestgreen", "limegreen", "mediumspringgreen"]
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plot_colors)

plt.figure(figsize=(15,25));
plt.plot(IncidentsByYearByPark, label=IncidentsByYearByPark.columns, marker="o", mew=8, linewidth=4);
plt.xlabel("Incident Date", fontweight="bold", size = 22);
plt.ylabel("Number of Incidents", fontweight="bold", size = 22);
plt.xticks(size=16)
plt.yticks(size=16)
plt.legend(loc="upper left", prop={"size":16});
plt.title("Number of Incidents Per Year, by National Park", fontweight="bold", size = 30);
plt.figure().set_figheight(20);
plt.show();

<Figure size 640x2000 with 0 Axes>


plot_colors = ["mediumaquamarine", "darkcyan", "deepskyblue", "firebrick", "mediumpurple", "darkorchid", "violet", "magenta", "deeppink", "palevioletred", "rosybrown", "lightcoral", "red", "sienna", "saddlebrown", "peru", "darkorange", "tan", "goldenrod", "gold", "darkkhaki", "olive", "yellowgreen", "olivedrab", "chartreuse", "darkseagreen", "palegreen", "forestgreen", "limegreen", "mediumspringgreen"]
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plot_colors)

plt.figure(figsize=(15,25));
plt.plot(IncidentsByMonth, label=IncidentsByYear.columns, marker="o", mew=8, linewidth=4);
plt.xlabel("Incident Date", fontweight="bold", size = 22);
plt.ylabel("Number of Incidents", fontweight="bold", size = 22);
plt.xticks(rotation="vertical", size=16)
plt.yticks(size=16)
plt.legend(loc="upper left", prop={"size":22});
plt.title("Number of Incidents Per Month of the Year, by Incident Type", fontweight="bold", size = 30);
plt.figure().set_figheight(20);
plt.show();

<Figure size 640x2000 with 0 Axes>


#If needed to reset color cycler back to default
#import matplotlib as mpl
#from cycler import cycler
#mpl.rcParams['axes.prop_cycle'] = cycler(color='bgrcmyk')

	UniqueID	Incident Number	Incident Date	Field Unit	Protected Heritage Area	Incident Type	Latitude Public	Longitude Public	Within Park	Total Staff Involved	Total Staff Hours	Species Common Name	Sum of Number of Animals	Animal Health Status	Cause of Animal Health Status	Animal Behaviour	Reason for Animal Behaviour	Animal Attractant	Deterrents Used	Animal Response to Deterrents	Activity Type_Driving	Activity Type_nan	Response Type_Dispose Carcass	Response Type_Investigate Incident	Response Type_Monitor - patrol
0	BAN2010-0003.3	BAN2010-0003	2010-01-01	Banff Field Unit	Banff National Park of Canada	Human Wildlife Interaction	51.161093	-115.593386	Yes	1.0	2.33	Coyote	2.0	Healthy	nan	Avoidance	Surprise	Prey animal (natural)	Presence of Officer/Person	nan	0.0	1.0	1.0	1.0	1.0
1	BAN2010-0003.2	BAN2010-0003	2010-01-01	Banff Field Unit	Banff National Park of Canada	Human Wildlife Interaction	51.161093	-115.593386	Yes	1.0	2.33	Elk	1.0	Dead	Predation	nan	nan	nan	nan	nan	0.0	1.0	1.0	1.0	1.0
2	BAN2010-0003.1	BAN2010-0003	2010-01-01	Banff Field Unit	Banff National Park of Canada	Human Wildlife Interaction	51.161093	-115.593386	Yes	1.0	2.33	Wolf	3.0	Not Located	nan	nan	nan	Prey animal (natural)	nan	nan	0.0	1.0	1.0	1.0	1.0
3	JNP2010-0011.1	JNP2010-0011	2010-01-01	Jasper Field Unit	Jasper National Park of Canada	Rescued/Recovered/Found Wildlife	53.139120	-117.964219	Yes	1.0	1.00	White-tailed Deer	1.0	Dead	Collision	nan	nan	nan	nan	nan	1.0	0.0	1.0	0.0	0.0
4	JNP2010-0015.1	JNP2010-0015	2010-01-01	Jasper Field Unit	Jasper National Park of Canada	Attractant	53.050492	-118.073612	Yes	1.0	2.50	None	0.0	nan	nan	nan	nan	Grain	nan	nan	1.0	0.0	1.0	0.0	0.0

This code file looks into Research Questions 1 & 2:¶

Here we explore and analyze patterns in the data by plotting frequency histograms and time series plots looking at incident types by human activity, animals involved, location, and time of year.¶

The following section generates basic frequency histograms to view the trends in "Incident Types" over various other variables such as human activities, animals involved, cause of animal behaviour, location, etc.¶

The following section generates various plots that dig deeper into features found interesting in the histograms¶

The following section generates time series plots looking at patterns in Incident Type and/or Frequency and location and time of year.¶